#think2019 - CrowdChat

Learn from Cloud & AI Experts

IBM experts discuss a faster, more secure journey to cloud, how to accelerate your path to AI, and what you can learn at the upcoming Think 2019 event.

#think2019 @ericminick @ralphfinos @franklybriana @plburris @rreinitz @lawtonbill @jim_farrow @ramvennam @lori_pb @jeff_brent @bobspory @ankita03pandey @pwmccaffrey @wikibon @nottycode

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How is your enterprise modernizing your data and integration architecture to accommodate a growing mix of clouds, SaaS, and traditional data sources on and off premises? https://www.crowdchat.net/s/45sdy

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Peter McCaffrey

by evolving to an "Agile Integration Architecture" that rethinks people, process, and technology. Learn more : https://www.ibm.com/cloud/integration

IBM Cloud Integration
Learn how IBM Cloud Integration — including cloud integration services, hybrid cloud integration and cloud data integration — help you access and use critical data with API, application, message and data integration.

The key is implementing an IA architecture that enables moving code to distributed data.

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guidance available modernizing data & data as a differentiator https://www.ibm.com/cloud/garage/architectures/dat...

https://www.ibm.com/cloud/garage/architectures/dataAnalyticsArchitecture

I'm seeing many companies moving their ECM content into the public cloud as part of their modernization strategy. Check out the Business Automation Content Services on Cloud details in the Digital Business Automation sessions at Think.

There's another factor to consider here, the scores of real-time transactional government tax mandates that require the digitaltransformation of core financial solutions to keep up with the digitization of tax. Hence the first MarketScape on the topic released this week

...MarketScape on the topic this week.

Does your analytics strategy presume to move data to analytics or analytics to data? Why? https://www.crowdchat.net/s/35rfn

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Data Gravity rules! You bring analytics to data, that is the most optimal

Analytics to data. Any data movement or copying is expensive and leads to all kinds of issues (lineage, quality, latency, higher resource utilization and cost)

always move Analytics to Data .. that's been our mantra . Data gravity should dictate your strategy. Moving against the gravity means you would end up spending a ton of resources / money & is not sustainable

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Anantha Narasimhan

Definitely analytics coming to data - so faster decision can be taken at source or close to it

Especially the world of multi-cloud strategy this is critical that we keep data where it is, thus technology like data virtualization, having governance built in to trust the data drives to trusted AI

Anantha Narasimhan

Trusted.. and business-ready data

Here's what Hemanth Manda, IBM and James Wade, Guidewell had to say on theCUBE recently about moving data from the mainframe to the cloud for analytics: James Wade, Guidewell: Cloud-First Strategy Must Include Mainframe

Hemanth Manda, IBM & James Wade, Guidewell | Change the Game: Winning With AI 2018
"Our mainframe is still sort of a cloud-like infrastructure."

Carlo Appugliese

in my opinion, Do your analytics where the data is if you can.. There is no value in moving lots of data, but there is significant business value in doing more analytics with your data. Its all about rate and pace of AI projects..

Data is growing exponentially within an enterprise. Moving becomes an avoidable expense if you can bring analytics to your data!

distributed data for sure - it's all over the place

In-situ/in-database analytics is a key foundation fo the big data revolution. Data gravity. Now with the edge looming larger as a data source, analytics is moving closer to those nodes and getting more sophisticated there. Distributed AI.

Data in volume is costly to move & takes a lot of time. Data loses value over time. so, it is usually much cheaper to move code to data than data to code. This is especially true for operational AI/analytics, which should be moved close to data source where possible.

Anantha Narasimhan

btw, there is an exciting session on Data Modernization strategy in a Multicloud World - by Madhu Kochar

Anantha Narasimhan

https://myibm.ibm.com/events/think/all-sessions/se...

@Matthias_Funke How much of a trend is IBM seeing toward distributed edge/client-based AI model training? What do your solutions offer to support that practice for mobile, IoT, edge, embedded apps?

Anantha Narasimhan

And another on Data Virtualization: https://myibm.ibm.com/events/think/all-sessions/se...

In my experience, companies already collecting data find value in turning data into analytics, whereas companies developing new products or services find more value in using analytics for data. The best #datascience teams needs to find the balance in doing both

It is interesting to observe that when AI systems are deployed, 90%+ of the code is in operational AI, rather than ML model development.

check this out: https://www.ibm.com/analytics/data-virtualization?...

@hkmanda Agreed. But what is the tipping point when the abundance of high-performance parallel compute in the cloud makes it faster and less costly (even with bandwidth costs/constraints) to do high-powered AI (training etc.) there?

@AnanthRN For more on business-ready data, don’t miss the Digital Transformation: A Business Ready Data Hub for Advanced Analytics session at Think 2019 happening Friday, February 15th at 9:30amPST on the Data & AI Campus.

depends! When doing #ML #AI, for compute intensive scenarios like human genome sequencing take data to compute. For data intensive scenarios (especially input), bring compute to data. #rethink

@madhu_kochar not always, see my response:)

Many #businesses are organized as #functional, #multidivisional, or #matrix. Hence, #data siloes naturally happen and continue to happen. Bringing analytics to data wherever it resides can be more economical. Move data when it is essential like for #protection.

Rob Thomas, GM of IBM Analytics, has said there is no AI without an IA (Information Architecture). How are you modernizing your data estate—the organization of your data assets—to get ready for an AI and multicloud world? https://www.crowdchat.net/s/05rda

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I think that he's really nailing the core AI (and ML) angle meta data or information that feeds AI engines is super important. If companies get this right then ML and AI soar to new heights

yes that's axiomatic

1. There are two sources of AI solutions – internal, and external, the normal make or buy decision. For products and services owned, it is vital that data is collected about those services in IA. However, there are many technologies it would be easier to buy.

Jason Tavoularis

Of course! AI requires data. if there's no infrastructure, there can't be much data, so you can't expect the AI to be very smart

Anantha Narasimhan

Our customers are looking at AI to help drive digital and potentially business transformation. At the core of AI are a) People & Culture, b) Process, c) Data

Here's what Rob Thomas had to say on theCUBE recently on the journey to AI in the multicloud: >> I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey tow...

Rob Thomas, IBM | Change the Game: Winning With AI 2018
"I kind of think of it as a maturity curve. So when I go talk to clients, I say, look, you need to be on a journey towards AI. I think probably nobody disagrees that they need something there, the qu…

I see this question come up everywhere. Modernization to gain agility, new insights faster, and have more people and business application benefit from it.

Anantha Narasimhan

With data present all across the organization, getting a good handle on it is the very first step... Collect, Organize and then Analyze data. and then Infuse AI models in order to operationalize

Having talked to a number of customers and business partners, this is an issue everyone is grappling with and we are addressing it through our new platform offering ICP for Data , an integrated data and AI platform for multi-cloud

Carlo Appugliese

We work with clients on their Data Science Journey and biggest factor to winning with AI is to make sure you plan for 3 things.. The right skills, the right process/ culture and finally the correct tools..

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Anantha Narasimhan

ML is a great enabler for AI. We need to remember that AI can help us win quickly.. or fall flat quickly. Because if the data is not of good quality, the models will throw up bad insights

There's no practical AI without data quality, governance, prep, and training in a high-performance data lake. Modernizing your data estate in the multicloud for AI demands an industrialized DevOps approach that automates much/most of these processes.

very often one needs to start at the bottom of the AI ladder and the question: How can I collect all the data I need, and make it accessible to the right people, at the right time? And how can i integrate data assets across different locations and data sources?

Anantha Narasimhan

The Garbage-in-Garbage-out phrase

For example, if you are wanting to ensure that only employees are entering enterprise premises, there will be many enterprises with the same problem, and many solutions to purchase.

Hello Everyone ! This is Omar from India Software Labs, Hyderabad.

Every client discussion starts with this dialog... and very critical to have a trusted analytics data foundation. It starts with know your data, trust your data and use your data to further drive insights

@JasonTavoularis Yes. AI can't be smart if data scientists can find the right data to drive feature engineering etc. Likewise, AI models can't do their jobs with high confidence without upfront and ongoing training from fresh operational data.

Couldn't agree more. Quality of AI models is directly proportional to the quality of data used to train the model. Without an information architecture to serve high-quality data, the AI models can be inconsistent, irrelevant or worse biased.

@AnanthRN Regarding people & culture for AI, how are you fostering AI skills throughout your developer and business analyst teams?

IMO, he future for analytics is real-time results. This means fast execution of operational AI/Analytics near the data. It also means low-latency connections between applications wanting to automate processes and the AI/analytics required.

@AnanthRN Right. Infusing AI into the business requires that an operationalized data science pipeline with a strong real-time/streaming CI/CD workflow.

@hkmanda If anyone is looking to learn more about ICP for Data, be sure to check-out session #2571, titled: Change the Game: Learn How to Win with AI happening on Wednesday, February 13th at 1:30pmPST in the Large Theater on the Data & AI Campus.

How are cloud, Agile development, and DevOps altering the relationship between your business and technology groups? https://www.crowdchat.net/s/75sba

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DevOps eradicates silos and requires stakeholders across business units to interact with one another, so it helps to establish mutual understanding and empathy as core team values that enable individuals to move toward a common goal.

I have to say that organizational change to an open culture is what slows adoption of new technology and cloud. A fair amount of my day job is focused in this. This is what we do at the Garage with our Garage Method. https://www.ibm.com/cloud/garage/category/practice...

https://www.ibm.com/cloud/garage/category/practices

we find they have big impact on the relationship for the good - our #ibmgaragemethod has a big focus on it -https://www.ibm.com/cloud/garage/category/envision

https://www.ibm.com/cloud/garage/category/envision

@rbarcia Interesting. A more open culture slows adoption new technology? Any particular cause?

@rbarcia When working on culture, how much of that is through working to change the org chart vs establishing new activities/ceremonies vs using tooling to manipulate culture?

This environment enable the business to be directly part of the development and integration process. This can lead to rapid adoption by the business - IMO more important than rapid coding!

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this video gives a client POV from Bendigo Adelaide Bank http://bit.ly/2zgpnYy

Bendigo and Adelaide Bank Find Rapid Innovation at IBM Bluemix Garage
Hear how a banking client's experience in the IBM Bluemix Garage produced surprising outcomes that turned their typically long development process into a rap...

The process of getting to an "Open Culture" and still worry about things like compliance for example.

@rbarcia Too many things at once, then. Thanks.

@EricMinick My experience is that the org chart is irrelevant to achieving meaningful change. You need the people doing the work to want to do the work and buy into the change and a new org chart can't fix that.

@rbarcia What DevOps workflows are conducive to building and iterating a "minimal viable product" for cloud services? Does adding serverless & Kubernetes to the scope of an MVP impede that process?

@rbarcia so not the culture being open vs closed, but the willingness of the culture to adopt open technologies.

@rbarcia What compromises must be made in open DevOps cultures to ensure compliance? Something must get scoped out of any process to keep it compliant with any spec?

@jameskobielus Certain industries like insurance have to do HIPPA checks, etc...

@rbarcia yes, just today I had a question from someone concerned that WebSphere Liberty was "less enterprise" because we open sourced most of it as Open Liberty. Of course the idea that open source is less enterprise-ready is a little old-fashioned.

@plburris great presentation from CIO of AA on change & benefits is getting from cloud & delivery transformation is at the core https://www.youtube.com/watch?v=djs39v3rQtA